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No.Model NameDescription
1Yolo-V5Real-time object detection using anchor-based detection heads.
1

Download Required Files

FileSave as
Modelyolov5_float.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolov5_float.tflite            <user>@<device-ip>:$HOME/models/
scp yolov8.json                    <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4      <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolov5_float.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=gpu ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME settings="{\"confidence\": 70.0}" ! text/x-raw ! queue ! obj_mux.
2Person-Foot-DetectionSpecialized foot and person localization for tracking pipelines.
1

Download Required Files

FileSave as
Modelperson_foot_detection_float.tflite
foot_track_net.jsonfoot_track_net.json
foot_track_net_settings.jsonfoot_track_net_settings.json
ai_demo_sample.mp4ai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp person_foot_detection_float.tflite       <user>@<device-ip>:$HOME/models/
scp foot_track_net.json                      <user>@<device-ip>:$HOME/labels/
scp foot_track_net_settings.json             <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4                            <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=person_foot_detection_float.tflite
export LABELS_NAME_1=foot_track_net.json
export LABELS_NAME_2=foot_track_net_settings.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=gpu ! queue ! \
qtimlpostprocess module=qpd labels=$HOME/labels/$LABELS_NAME_1 settings=$HOME/labels/$LABELS_NAME_2 ! text/x-raw ! queue ! obj_mux.
1

Download Required Files

FileSave as
Modelperson_foot_detection_w8a8.tflite
foot_track_net.jsonfoot_track_net.json
foot_track_net_settings.jsonfoot_track_net_settings.json
ai_demo_sample.mp4ai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp person_foot_detection_w8a8.tflite        <user>@<device-ip>:$HOME/models/
scp foot_track_net.json                      <user>@<device-ip>:$HOME/labels/
scp foot_track_net_settings.json             <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4                            <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=person_foot_detection_w8a8.tflite
export LABELS_NAME_1=foot_track_net.json
export LABELS_NAME_2=foot_track_net_settings.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=qpd labels=$HOME/labels/$LABELS_NAME_1 settings=$HOME/labels/$LABELS_NAME_2 ! text/x-raw ! queue ! obj_mux.
3Yolo-XAnchor-free YOLO variant with decoupled detection head.
1

Download Required Files

FileSave as
Modelyolox_w8a8.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolox_w8a8.tflite              <user>@<device-ip>:$HOME/models/
scp yolov8.json                    <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4      <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolox_w8a8.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
4YOLOv11-DetectionLatest YOLO generation with enhanced feature extraction.
1

Download Required Files

FileSave as
Modelyolov11_det_float.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolov11_det_float.tflite       <user>@<device-ip>:$HOME/models/
scp yolov8.json                    <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4      <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolov11_det_float.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=gpu ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
1

Download Required Files

FileSave as
Modelyolov11_det_w8a8.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolov11_det_w8a8.tflite        <user>@<device-ip>:$HOME/models/
scp yolov8.json                    <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4      <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolov11_det_w8a8.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
5Yolo-v7High-accuracy detection with re-parameterizable convolutions.
1

Download Required Files

FileSave as
Modelyolov7_float.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolov7_float.tflite                 <user>@<device-ip>:$HOME/models/
scp yolov8.json                         <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4           <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolov7_float.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=gpu ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
1

Download Required Files

FileSave as
Modelyolov7_w8a8.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolov7_w8a8.tflite             <user>@<device-ip>:$HOME/models/
scp yolov8.json                    <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4      <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolov7_w8a8.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
6YOLOv8-DetectionStreamlined single-stage detector with CSP bottleneck backbone.
1

Download Required Files

FileSave as
Modelyolov8_det_float.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolov8_det_float.tflite        <user>@<device-ip>:$HOME/models/
scp yolov8.json                    <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4      <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolov8_det_float.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=gpu ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
1

Download Required Files

FileSave as
Modelyolov8_det_w8a8.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolov8_det_w8a8.tflite         <user>@<device-ip>:$HOME/models/
scp yolov8.json                    <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4      <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolov8_det_w8a8.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
7YOLOv10-DetectionNMS-free dual-label assignment for end-to-end detection.
1

Download Required Files

FileSave as
Modelyolov10_detection_w8a8.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolov10_detection_w8a8.tflite  <user>@<device-ip>:$HOME/models/
scp yolov8.json                    <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4      <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolov10_detection_w8a8.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
1

Download Required Files

FileSave as
Modelyolov10_detection_float.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolov10_detection_float.tflite  <user>@<device-ip>:$HOME/models/
scp yolov8.json                     <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4       <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolov10_detection_float.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=gpu ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
8DETR-ResNet101Transformer-based detector with deeper ResNet-101 backbone.
1

Download Required Files

FileSave as
Modeldetr_resnet101_float.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp detr_resnet101_float.tflite  <user>@<device-ip>:$HOME/models/
scp yolov8.json                  <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4    <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=detr_resnet101_float.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME settings="{\"confidence\": 70.0}" ! text/x-raw ! queue ! obj_mux.
9DETR-ResNet50Transformer-based detector with ResNet-50 backbone.
1

Download Required Files

FileSave as
Modeldetr_resnet50_float.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp detr_resnet50_float.tflite  <user>@<device-ip>:$HOME/models/
scp yolov8.json                 <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4   <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=detr_resnet50_float.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.
10Conditional-DETR-ResNet50Conditional cross-attention DETR for faster convergence.
1

Download Required Files

FileSave as
Modelconditional_detr_resnet50_float.tflite
coco_labels.jsoncoco_labels.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp conditional_detr_resnet50_float.tflite  <user>@<device-ip>:$HOME/models/
scp coco_labels.json                        <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4               <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=conditional_detr_resnet50_float.tflite
export LABELS_NAME=coco_labels.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME settings="{\"confidence\": 50.0}" ! text/x-raw ! queue ! obj_mux.
11RF-DETRRegion-aware feature DETR with improved small-object detection.
1

Download Required Files

FileSave as
Modelrf_detr_float.tflite
coco_labels.jsoncoco_labels.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp rf_detr_float.tflite            <user>@<device-ip>:$HOME/models/
scp coco_labels.json                <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4       <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=rf_detr_float.tflite
export LABELS_NAME=coco_labels.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME settings="{\"confidence\": 70.0}" ! text/x-raw ! queue ! obj_mux.
12RTMDetReal-time instance-level detector with dynamic label assignment.
1

Download Required Files

FileSave as
Modelrtmdet_float.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp rtmdet_float.tflite        <user>@<device-ip>:$HOME/models/
scp yolov8.json                <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4  <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=rtmdet_float.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp,log_level=(string)1;" ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME settings="{\"confidence\": 50.0}" ! text/x-raw ! queue ! obj_mux.
13Yolo-RMulti-scale object detection with implicit knowledge distillation.
1

Download Required Files

FileSave as
Modelyolor_float.tflite
yolov8.jsonyolov8.json
Input videoai_demo_sample.mp4
If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip
2

Copy Files to Device

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.

ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolor_float.tflite         <user>@<device-ip>:$HOME/models/
scp yolov8.json                <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4  <user>@<device-ip>:$HOME/media/
3

Connect to device

# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip>
4

Set Environment Variables

Qualcomm Linux
export MODEL_NAME=yolor_float.tflite
export LABELS_NAME=yolov8.json
export SRC_VIDEO_NAME=ai_demo_sample.mp4
5

Run the Pipeline

gst-launch-1.0 -e --gst-debug=2 \
filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! \
v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! \
tee name=t ! qtimetamux name=obj_mux ! qtivoverlay ! waylandsink fullscreen=true sync=true \
t. ! queue ! qtimlvconverter ! queue ! \
qtimltflite model=$HOME/models/$MODEL_NAME delegate=gpu ! queue ! \
qtimlpostprocess module=yolov8 labels=$HOME/labels/$LABELS_NAME ! text/x-raw ! queue ! obj_mux.